Underactuated multi-fingered humanoid hands easily and safely accomplish a wide variety of grasping tasks in human-centric scenarios, questioning about its performance in ordinary manipulation tasks after the grasp of an object. High state-space dimensionality inherent to dexterous fully actuated multi-finger manipulators poses control difficulties that may be unnecessary in some typical activities, which creates a window of opportunity for underactuated end-effectors to be employed. We propose a two-stage pipeline system to address in-hand manipulation of an object in a real-world scenario, composed of an off-the-shelf category-level object pose estimator to deal with the previously unseen item and a model-free Deep Reinforcement Learning (DRL) algorithm aided by Imitation Learning (IL) to get more robust and natural movements. Our experiments show a positive learning curve for the in-hand object rotation task, dealing reliably with real environment problems such as sample inefficiency and noisy object estimations.


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    Title :

    In-Hand Manipulation of Unseen Objects Through 3D Vision


    Additional title:

    Lect. Notes in Networks, Syst.



    Conference:

    Iberian Robotics conference ; 2022 ; Zaragoza, Spain November 23, 2022 - November 25, 2022



    Publication date :

    2022-11-19


    Size :

    12 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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